volume | PIER Journals

Abstract

Microwave Staring Correlated Imaging (MSCI) technology can obtain high-resolution images in staring imaging geometry by utilizing the temporal-spatial stochastic radiation field. In MSCI, sparse-driven approaches are commonly used to reconstruct the target images when the radiation fields are accurately calculated. However it is challenging to compute radiation filed with high precision due to existence of random phase errors in MSCI systems. Therefore, in this paper, a self-calibration method is proposed to handle the problem. Specifically, a two-step self-calibration framework is applied which alternately reconstructs the target image and estimates the random phase errors. In the target image reconstruction step, sparse-driven approaches are utilized, while in the random phase errors calibration step, an adaptive learning rate method is adopted. Moreover, the batch--learning strategy is utilized to reduce computation burden and obtain effective convergence performance. Numerical simulations verify the advantage of the proposed method to obtain good imaging results and improve random phase errors correction performance.

1. Lord, R. T. and M. R. Inggs, "High resolution SAR processing using stepped-frequencies," 1997 IEEE International Geoscience and Remote Sensing, 1997, IGARSS '97, Remote Sensing — A Scientific Vision for Sustainable Development, Vol. 1, 490-492, 1997.

2. Patel, V. M., G. R. Easley, D. M. Healy, Jr., and R. Chellappa, "Compressed synthetic aperture radar," IEEE Journal of Selected Topics in Signal Processing, Vol. 4, No. 2, 244-254, 2010.

3. Zhu, X. X., S. Montazeri, C. Gisinger, R. F. Hanssen, and R. Bamler, "Geodetic SAR tomography," IEEE Transactions on Geoscience and Remote Sensing, Vol. 54, No. 1, 18-35, 2016.

4. Ma, Y., X. He, Q. Meng, B. Liu, and D. Wang, "Microwave staring correlated imaging and resolution analysis," Geo-Informatics in Resource Management and Sustainable Ecosystem, 737-747, Springer, 2013.

5. Dunkel, R., R. Saddler, and A. W. Doerry, "Synthetic aperture radar for disaster monitoring," Radar Sensor Technology XV, 125-134, 2011.

6. Madsen, S., N. Edelstein, W. Didomenico, and L. D. Labrecque, "A geosynchronous synthetic aperture radar; for tectonic mapping, disaster management and measurements of vegetation and soil moisture," IEEE 2001 International Geoscience and Remote Sensing Symposium, 2001, IGARSS '01, Vol. 1, 447-449, 2002.

7. Guo, Y., X. He, and D. Wang, "A novel super-resolution imaging method based on stochastic radiation radar array," Measurement Science and Technology, Vol. 24, No. 7, 074013, 2013.

8. Li, D., X. Li, Y. Qin, Y. Cheng, and H. Wang, "Radar coincidence imaging: An instantaneous imaging technique with stochastic signals," IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, No. 4, 2261-2277, 2014.

9. Cheng, Y., X. Zhou, X. Xu, Y. Qin, and H. Wang, "Radar coincidence imaging with stochastic frequency modulated array," IEEE Journal of Selected Topics in Signal Processing, Vol. 11, No. 2, 414-427, 2017.

10. Guo, Y., D. Wang, and C. Tian, "Research on sensing matrix characteristics in microwave staring correlated imaging based on compressed sensing," 2014 IEEE International Conference on Imaging Systems and Techniques (IST), 195-200, IEEE, 2014.

11. Zhou, X., H. Wang, Y. Cheng, Y. Qin, and H. Chen, "Radar coincidence imaging for off-grid target using frequency-hopping waveforms," International Journal of Antennas & Propagation, Vol. 2016, 1-16, 2016.

12. Zhou, X., H. Wang, Y. Cheng, Y. Qin, and H. Chen, "Waveform analysis and optimization for radar coincidence imaging with modeling error," Mathematical Problems in Engineering, 2017, 2017.

13. Liu, B. and D. Wang, "Orthogonal radiation field construction for microwave staring correlated imaging," Progress In Electromagnetics Research M, Vol. 57, 139-149, 2017.

14. Yuan, B., Y. Guo, W. Chen, and D. Wang, "A novel microwave staring correlated radar imaging method based on bi-static radar system," Sensors, Vol. 19, No. 4, 879, Feb. 2019.

15. Zhou, X., H. Wang, Y. Cheng, and Y. Qin, "Sparse auto-calibration for radar coincidence imaging with gain-phase errors," Sensors, Vol. 15, No. 11, 27611-27624, 2015.

16. Xu, X., X. Zhou, Y. Cheng, and Y. Qin, "Radar coincidence imaging with array position error," 2015 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Ningbo, China, 2015.

17. Cao, K., X. Zhou, Y. Cheng, and Y. Qin, "Improved focal underdetermined system solver method for radar coincidence imaging with model mismatch," Journal of Electronic Imaging, Vol. 26, No. 3, 033001, 2017.

18. Cao, K., X. Zhou, Y. Cheng, B. Fan, and Y. Qin, "Total variation-based method for radar coincidence imaging with model mismatch for extended target," Journal of Electronic Imaging, Vol. 26, No. 6, 063007, 2017.

19. Zhang, F., X. Liu, X. Zhou, X. Wang, and W. Liu, "Autofocus technique for radar coincidence imaging with model error via iterative maximum a posteriori," The Journal of Engineering, Vol. 2019, No. 19, 5837-5840, 2019.

20. Zhou, X., H. Wang, Y. Cheng, and Y. Qin, "Radar coincidence imaging with phase error using bayesian hierarchical prior modeling," Journal of Electronic Imaging, Vol. 25, No. 1, 013018, 2016.

21. Tian, C., B. Yuan, and D. Wang, "Calibration of gain-phase and synchronization errors for microwave staring correlated imaging with frequency-hopping waveforms," 2018 IEEE Radar Conference (RadarConf18), 1328-1333, 2018.

22. Cao, K., X. Zhou, Y. Cheng, Y. Qin, and K. Liu, "A method for radar coincidence imaging with model errors," 2017 International Workshop on Electromagnetics: Applications and Student Innovation Competition, 144-146, 2017.

23. Onhon, N. and M. Cetin, "A sparsity-driven approach for joint sar imaging and phase error correction," IEEE Transactions on Image Processing, Vol. 21, No. 4, 2075-2088, 2012.

24. Zhao, L., L. Wang, G. Bi, and L. Yang, "An autofocus technique for high-resolution inverse synthetic aperture radar imagery," IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, No. 10, 6392-6403, 2014.

25. Ding, L. and W. Chen, "MIMO radar sparse imaging with phase mismatch," IEEE Geoscience and Remote Sensing Letters, Vol. 12, No. 4, 816-820, 2015.

26. Wang, L., L. Zhao, G. Bi, C. Wan, and L. Yang, "Enhanced ISAR imaging by exploiting the continuity of the target scene," IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, No. 9, 5736-5750, 2014.

27. Wu, Q., Y. D. Zhang, M. G. Amin, and B. Himed, "Multi-task bayesian compressive sensing exploiting intra-task dependency," IEEE Signal Processing Letters, Vol. 22, No. 4, 430-434, 2015.

28. Wan, C., "Sparse representation-based isar imaging using markov random fields," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 8, No. 8, 1-13, 2014.

29. Wang, H., Y. Qin, Y. Cheng, and X. Zhou, "Radar coincidence imaging by exploiting the continuity of extended target," IET Radar Sonar & Navigation, Vol. 11, No. 1, 60-69, 2017.

30. Zeiler, M. D., "Adadelta: An adaptive learning rate method," Computer Science, 2012.

31. Bottou, L., "Large-scale machine learning with stochastic gradient descent," Proceedings of COMPSTAT’2010, 177-186, Springer, 2010.

32. Hoffman, M. D., D. M. Blei, C. Wang, and J. Paisley, "Stochastic variational inference," The Journal of Machine Learning Research, Vol. 14, No. 1, 1303-1347, 2013.

Dr. Bo Yuan

Dr. Zheng Jiang

Dr. Jianlin Zhang

Dr. Yuanyue Guo

Dr. Dongjin Wang